Proof hacking

I recently lectured at an EST training workshop in Fischbachau, Germany. There were also a number of student talks, one of which was given by Luca Chiarabini from Munich. He talked about extraction of programs from proofs, using (a variant of) Curry-Howard isomorphism, also known as propositions-as-types. He had some very interesting ideas which were obviously related to old programming tricks, but he approached them from the logical point of view, rather than the programmer’s point of view. It got me thinking about how to write certain recursive programs as proofs. Since it is a nice application of program extraction, I want to share it with you here.

First, if you have never heard of propositions-as-types or Curry-Howard, you should get acquantied and come back here. Or you can just read on and pretend you understand (a strategy which works surprisngly well, after a while you can convince even yourself that you understand).

You have heard that in the propositions-as-types correspondence conjunction is cartesian product, and implication is function types. Have you every asked what induction on natural numbers correspond to? First recall that the proof rule for induction looks like this:

You should read this “fraction” as follows: if `x_0` is a proof of `P(0)` and `f` is a proof of `forall n in NN.(P(n) => P(n+1))`, then `ind\ x_0\ f` is a proof of `forall n in NN. P(n)`. Then the question is, what does `ind` compute? According to propositions-as-types, `ind\ x_0\ f n` is supposed to give a proof of `P(n)`. If you stare at it for long enough you will realize that `ind` is just definition by recursion, i.e., `ind\ x_0\ f\ n` computes `f (f (f … f(x_0) …))` with `f` nested `n` times. We could program `ind` in ML (ocaml, actually) like this:

let rec ind x0 f n =
if n = 0 then x0 else f n (ind x0 f (n-1))

For the purposes of this discussion I want to have precise control over how recursive calls happen, so I am going to thunk them, and also insert a print statement so that we can see what is going on:

Now we can use ind to define a recursive function whose value at `n+1` depends on the value at `n`. All we need to do is to give the base case `x_0` and a function which gets us from the value at `n-1` to the value at `n` (this function receives `n` and a thunk`p`. Whenever it evaluates `p()`, it gets the value at `n-1`). For example, to define the function pow, which computes powers of 2 by recursive definition `pow(0) = 1` and `pow(n+1) = pow(n) + pow(n)`, we would write:

let pow = ind 1 (fun n p -> p() + p())

This way of programming is very stupid, of course. Nobody would ever compute powers of 2 this way, because we make two recursive calls at each step and end up with an exponential algorithm:

The point of program extraction is to turn such stupid programs into smart ones, which is what Luca talked about. Suppose you are not allowed to change the definition of the pow function (because it is defined just so as a mathematical function and many useful theorems are proved from it, and if you fiddled with the definition other things would break). Can we still make a faster pow by changing ind instead? Sure we can, just pre-evaluate every recursive call:

The important bit is that we did not change pow, only ind. Let us now turn the propositions-as-types around and ask, which proof does ind' correspond to? We have a problem here because we used a let construct in ind', and it is not clear what it corresponds to in the propositions-as-types (at least not when we want to distinguish call-by-value and call-by-name). So our first task is to rewrite ind' in pure `lambda`-calculus. In addition, ind' should not make any recursive calls, because we do not know what they correspond to, either. What we can use is the old ind, because we know that it corresponds to induction. With a bit of head scratching, I produced the following:

Good. It works on at least one example. I suppose the new ind' requires some explanation. The easiest way to explain what it is doing is to write it without the use of ind. This turns into the following recursive function:

Observe that the job is really done by the helper function. Notice also that in each step helper decreases k and increases x. So in effect k and x are the same counter, but they count in different directions. The argument y is the previous value of f. Thus helper gets arguments `k`, `x`, `y` and then calls itself recursively with new arguments `k-1`, `x+1`,`f x (lambda ().y)`. Because helper is tail recursive, we can eliminate the recursion and rewrite it as a simple loop. The arguments become mutable variables when we do this:

You may or may not find this version easier to understand. Now let us return back to ind'. According to the Curry-Howard isomorphism, it corresponds to a proof.
What does this proof prove? Since ind' is just a variant of ind, they both prove the same thing, i.e., `forall n in NN. P(n)`. The body of ind' is of the form lemma n 0 x0 where lemma is the piece of code

ind (fun x y -> y) (fun () p x y -> p() (x+1) (f x (fun () -> y)))

Remebering that application corresponds either to modus ponens or instantiation of a universal statement, we would expect lemma to be the proof of something of the form

`forall n in NN. forall m in NN. (P(m) => Q(n,m))`

so that when we apply it to `n`, `0` and `x_0` (remeber that `x_0` is a proof of `P(0)`) we get
a proof of `Q(n,0)`. Since `Q(n,0)` is supposed to be `P(n)`, we take a guess that `Q(n,m)=P(n+m)`. This means that lemma should be a proof of

`forall n in NN. forall m in NN. (P(m) => P(n+m))`.

Since lemma is a proof by induction, we should check to see if its parts prove the base case and the induction step. The base case `n = 0` is `forall m in NN. (P(m) => P(0+m))`, which is indeed proved by fun x y -> y. The induction step is

This also is proved by fun () p x y -> p() (x+1) (f x (fun () -> y)), which I will try to explain in few word. Suppose p is a proof of `forall m in NN. (P(m) => P(n+m))` and y is a proof of `P(m)`. Then f m y is a proof of `P(m+1)` because f is a proof of `forall n in NN. (P(n) => P(n+1))`. So p (m+1) (f m y) proves `P(n+(m+1))`, as required.

The lesson learnt here is that smart programs may correspond to seemingly silly proofs. In our case ind' corresponds to proving `forall m in NN. P(m)` from `P(0)` and `forall k in NN.(P(k) => P(k+1))` by first proving the lemma

`forall n in NN. forall m in NN. (P(n) => P(n+m))`

and then applying it to the case `n = 0`. I find it higly non-obvious that proving such a lemma amounts to turning simple recursion to tail-recursion. What we did is in fact a special case of memoization or dynamic programming. Two obvious questions to ask are:

How do we generalize this idea to more general recursive functions?

How can we recognize proofs that encode tail-recursive functions?

I am sure Luca is going to tell us some day what the answers are.

Addendum:
I claimed it is not clear what let x = e1 in e2 means as a proof. This is not exactly so, since it is equivalent to (fun x -> e1) e2. So the let-construct is just the modus ponens rule. Another point worth mentioning is that if e is the proof of `P` then the thunk fun () -> e is the proof of `TT => P`.

2 thoughts on “Proof hacking”

The nice idea of Andrej presented in this post, was of great inspiration in writing:http://www.mathematik.uni-muenchen.de/~chiarabi/research/Papers/Articolo%20Bauer/versione_articolo.pdf
(next CIE08). The application of such proof transformation to inductive proofs on naturals
permit to extract “tail” recursive code. Other authors investigated such
possibility, like Penny Anderson(1994), but their method were extremely much more complicated.
Here the proposed proof transformation is clear and simple. There is a point that is not yet
fully investigated: how to link such transformation to CPS? CPS is the canonical program-transformation to obtain tail recursion in programming. I think is possible to write
a CPS on proofs, that take into account also proof by inductions, acting extractly as the
proof transformation presented here. Ciao, Luca